Parallel Genetic Algorithm in Combinatorial Optimization

نویسنده

  • Sankt Augustin
چکیده

Parallel genetic algorithms (PGA) use two major modiications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live i n a 2-D w orld. Selection of a mate is done by e a c h individual independently in its neighborhood. Secondly, each individual may improve its tness during its lifetime by e.g. local hill-climbing. The PGA is totally asynchronous, running with maximal eeciency on MIMD parallel computers. The search strategy of the PGA is based on a small number of intelligent and active individuals, whereas a GA uses a large population of passive individuals. We will show t h e p o wer of the PGA with two c o m binatorial problems-the traveling salesman problem and the m graph partitioning problem. In these examples, the PGA has found solutions of very large problems, which are comparable or even better than any other solution found by other heuristics. A comparison between the PGA search strategy and iterated local hill-climbing is made.

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تاریخ انتشار 1992